Project¶

Analysis Of Moja Global Datasets To Evaluate Crossriver Forest In Nigeria¶

Table of Contents¶

  • Introduction
  • Administrative Boundaries
  • Soil resources
  • Bioclimatic & Ecological Zone
    • 1. Biodiversity Hotspot
    2. Agroecological Zones
  • Land Cover
  • Climate
  • Conclusion

Introduction¶

About:

Cross-River State is located in Nigeria's South South geopolitical zone. It’s capital is Calabar, and it shares borders with Benue State to the north, Ebonyi State and Abia State to the west, Akwa Ibom State to the southwest, and Cameroon to the east. Cross-River State was formerly known as the South-Eastern State before being renamed in 1976.

Cross-River is the 19th largest in area and the 27th most populous state in Nigeria, with a population of over 3.8 million (as of 2016) and a land mass of approximately 21,265 km2. Currently, the state contains approximately 31% of Nigeria's total remaining tropical high forests.

Cross-River State currently has roughly 40% of Nigeria's remaining threatened Tropical Rainforests. These forests are one of the oldest rainforests in Africa, and the country's Largest remaining stand of comparably unlogged Tropical Rainforest. Cross-River state is also well-known for its ecotourism potential. The ecotourism industry is largely dependent on the Tropical Rainforest, which are home to species endemic to Cross-River State.

Geographically, the state is divided into two parts:

  • The Guinean forest-savanna mosaic (in the far north)
  • The Cross-Sanaga-Bioko coastal forests (that cover the majority of the state's interior).

The smaller ecoregions are the Central African mangroves in the far south of the country's coast and a part of the montane Cameroonian Highlands forests in the far northeast. The Cross River, which bisects Cross-River State's interior before forming much of the state's western border and flowing into the Cross-River Estuary, is the state's most prominent geographical feature. The Calabar and Great Kwa rivers, which flow from the inland Oban Hills before flanking Calabar and flowing into the Cross-River Estuary, are also significant. In the forested interior of the state are several biodiverse protected areas including the Cross-River National Park, Afi Mountain Wildlife Sanctuary, and Mbe Mountains Community Forest.

The Cross-river forest possesses vast features of a typical tropical rainforest ecosystem. The area harbours some African threatened species that are of paramount conservation relevance. Some of these tree species included Terminalia ivorensis, Pterocarpus soyauxii, Melicia excelsa, Bailonella toxisperma and Afzelia bipindensis . Besides the tree species, the forest equally contains animals of conservation significance. Amongst these are the wildlife populations of Preuss's red colobus, African forest buffalo, bat hawk, tree pangolin, grey-necked rockfowl, and West African slender-snouted crocodile along with some of Nigeria's last remaining Nigeria-Cameroon chimpanzee, drill, African forest elephant, and the Cross River gorilla (Gorilla gorilla diehli) also endemic to the area. The Forest resources of the state cover’s six ecological zones namely:-

  1. Mangrove forests with an area of 480Km2
  2. Swamp forest with an area of 520Km2
  3. Tropical High Forests including the National Park with an area of 7,290 Km2
  4. Plantations with an area of 460Km2
  5. Other forests with an area of 216Km2
  6. Other land uses have an area of 12,299 Km2

Cross River State has lost approximately 19% of its tropical high forests over the last decades due to insufficient Forestry Department funding, population and immigration growth, and plantation establishment.

Cross River State is rapidly developing its ecotourism potential and is widely regarded as Nigeria's premier tourist destination. The ability to balance, utilize, and preserve species, particularly those endemic to Cross River state, offers a solid foundation for environmental sustainability as well as significant money-making potential in the not-too-distant future.

Concerns:

Both divisions of the park are threatened by illegal logging, slash and burn farming and poaching. Eco-tourism may support efforts to preserve the park fauna. Assisting villagers in buffer zones to practice sustainable forestry also holds promise.

SOURCE:

  1. https://en.wikipedia.org/wiki/Cross_River_National_Park
  2. https://www.bradtguides.com/destinations/africa/nigeria/cross-river-forests-mountains/
  3. Forest Resources of Cross River State: Their Potentials, Threats and Mitigation Measures - www.iosrjournals.org
  4. Abang, S.O and Omolaubi, D.A. (1994); Marketing Margins in Non-Timber forest products trade in Cross River State of Nigeria.
  5. Akachukwu, AC (2007). Disappearing forest, the consequence and challenges of sustainable Development in Nigeria in Proceedings of 31st Annual Conference of the forestry Association of Nigeria – 25th November, 2006,

Importing Dataset¶

In [1]:
# install geopandas, this could take long
import geopandas as gpd
In [2]:
# for download and decompress the dataset
import os, zipfile, glob, io, requests

# for SIG and spatial info management
from shapely.geometry import Polygon, shape

# for SIG and spatial visualization
import folium
from folium import Choropleth
from folium.plugins import HeatMap

# for dataframes and numerical info management
import numpy as np
import pandas as pd

# for plot and graphs
import matplotlib.pyplot as plt
import seaborn as sns

# Ignore warning about missing/empty geometries
import warnings
warnings.filterwarnings('ignore', 'GeoSeries.notna', UserWarning)
plt.rcParams.update({'figure.max_open_warning': 0})
warnings.simplefilter("ignore", category=FutureWarning)

# for dependencies
%matplotlib inline

Administrative boundaries¶

Importing Neccessary Data¶

In [3]:
# importing the data
world_df = gpd.read_file("ne_110m_admin_0_countries.zip")
world_df.to_crs(epsg=4326, inplace=True)
In [4]:
# also the dataset is huge (162 features), so it need cleaning.
world_df = world_df[['CONTINENT','NAME_LONG', 'POP_EST', 'POP_YEAR', 'geometry']]
world_df.columns = world_df.columns.str.lower()
In [5]:
world_df.head()
Out[5]:
continent name_long pop_est pop_year geometry
0 Oceania Fiji 889953.0 2019 MULTIPOLYGON (((180.00000 -16.06713, 180.00000...
1 Africa Tanzania 58005463.0 2019 POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...
2 Africa Western Sahara 603253.0 2017 POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
3 North America Canada 37589262.0 2019 MULTIPOLYGON (((-122.84000 49.00000, -122.9742...
4 North America United States 328239523.0 2019 MULTIPOLYGON (((-122.84000 49.00000, -120.0000...
In [6]:
# features for plot
world_df['centroid'] = world_df.geometry.centroid # adding centroid for plotting names in the map

C:\Users\HP\AppData\Local\Temp\ipykernel_17432\1643154666.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  world_df['centroid'] = world_df.geometry.centroid # adding centroid for plotting names in the map

Plotting world map¶

In [7]:
# Plotting the map
ax = world_df.plot(figsize=(20,15), column= 'name_long', cmap= 'tab10', 
                   edgecolor= 'black', legend= True, 
                   legend_kwds={'loc': 'center left', 'ncol': 4, 'bbox_to_anchor':(1,0.5)})
ax.set_title('\nWORLD MAP View by Countries\n', fontsize=20)
plt.show()

Extracting African countries from the world data¶

In [8]:
# for the purpose of use this boundaries as a clipper, the african countries 
# (filtered on the CONTINENT feature) using pandas like dataframe management
africa_df = world_df.loc[world_df.continent == 'Africa',]

# fixing data
africa_df = africa_df.reset_index(drop=True)
# africa_df.lastcensus = africa_df.lastcensus.astype(str).replace({'-99':'1999'}).astype(int)

# features for plot
africa_df['popu'] = africa_df['pop_est'] / 1e6 # get the population in millions
In [9]:
# we show what the africa data looks like
africa_df.head()
Out[9]:
continent name_long pop_est pop_year geometry centroid popu
0 Africa Tanzania 58005463.0 2019 POLYGON ((33.90371 -0.95000, 34.07262 -1.05982... POINT (34.75299 -6.25773) 58.005463
1 Africa Western Sahara 603253.0 2017 POLYGON ((-8.66559 27.65643, -8.66512 27.58948... POINT (-12.13783 24.29117) 0.603253
2 Africa Democratic Republic of the Congo 86790567.0 2019 POLYGON ((29.34000 -4.49998, 29.51999 -5.41998... POINT (23.58296 -2.85028) 86.790567
3 Africa Somalia 10192317.3 2019 POLYGON ((41.58513 -1.68325, 40.99300 -0.85829... POINT (45.72670 4.75235) 10.192317
4 Africa Kenya 52573973.0 2019 POLYGON ((39.20222 -4.67677, 37.76690 -3.67712... POINT (37.79156 0.59597) 52.573973
In [10]:
# Plotting the map
ax = africa_df.plot(figsize=(10,15), column= 'name_long', cmap= 'Set2', 
                           edgecolor= 'black')
ax.set_title('\nAFRICA\n', fontsize=20)

# names in the map
props = dict(boxstyle='round', facecolor='linen', alpha=1)
for point in africa_df.iterrows():
  ax.text(point[1]['centroid'].x,
          point[1]['centroid'].y,
          point[1]['name_long'],
          horizontalalignment='center',
          fontsize=10,
          bbox=props)
ax.axis('off')
plt.show()

Plotting side by side maps showing contrast between african countries and their respective population (in millions)¶

In [11]:
# Plotting side by side maps
fig, axes = plt.subplots(1,2, figsize=(20,15))

# first map axes[0]
africa_df.plot(ax = axes[0], figsize=(10,15), column= 'name_long', cmap= 'bwr', 
                           edgecolor= 'black')
axes[0].set_title('\nAFRICA COUNTRIES\n', fontsize=25)
ax.axis('off')

# names in the map
props = dict(boxstyle='round', facecolor='linen', alpha=1)
for point in africa_df.iterrows():
  axes[0].text(point[1]['centroid'].x,
          point[1]['centroid'].y,
          point[1]['name_long'],
          horizontalalignment='center',
          fontsize=10,
          bbox=props)

# second map axes[1]
africa_df.plot(ax = axes[1], figsize=(10,15), column= 'popu', cmap= 'Greens', 
                           edgecolor= 'black', scheme='quantiles', legend= True)
axes[1].set_title('\nAFRICA COUNTRIES\n(VIEW BY POPULATION IN MILLIONS)\n', fontsize=20)

# names in the map
props = dict(boxstyle='round', facecolor='linen', alpha=1)
for point in africa_df.iterrows():
  axes[1].text(point[1]['centroid'].x,
          point[1]['centroid'].y,
          point[1]['name_long'],
          horizontalalignment='center',
          fontsize=10,
          bbox=props)
plt.axis('off')
plt.show()
  • Some of the most populated coutries are: South Africa, Nigeria, Egypt, Algeria, Ethopia, Sudan, Tanzania, Democratic republic of congo,uganda, kenya. while djibouti one of the least populated

Deep diving into boundaries by country in view¶

In [12]:
# Nigeria boundary dataset
boundary1 = 'https://datasets.mojaglobal.workers.dev/0:/Administrative/Boundaries/Level2%20by%20Country/NGA/NGA_AL2_Nigeria.json'

# download the data with request
boundary1_req = requests.get(boundary1).content

# load the json with geopandas and set crs
boundary_df = gpd.read_file(io.StringIO(boundary1_req.decode('utf-8')))
boundary_df = boundary_df[['name', 'geometry']]

# fix the georeference! the map seems to be cliped from a bad reference
boundary_df.to_crs(5389, inplace=True)
boundary_df.to_crs(4326, inplace=True) # now is WGS84 again!

# Plotting the map
ax = boundary_df.plot(figsize=(7,7), color= '#2E933C', edgecolor= '#ECBA82')
ax.set_title('Administrative boundaries In Nigeria\n', fontsize=15)

plt.axis('off')
plt.show()

Showing the nigerian map in africa¶

In [13]:
# Plotting Nigeria in Africa
ax = africa_df.plot(figsize=(10,10), color= '#ECBA82', edgecolor= 'white')
boundary_df.plot(ax=ax, color='#2E933C', edgecolor='white')
ax.set_title('\nVIEW OF Nigeria IN Africa\n', fontsize=20)

plt.axis('off')
plt.show()

Deep diving into specific forest in consideration by state (Ogun and Crossriver )¶

In [14]:
# Nigeria State choosed for review: oyo and ogun.

# state dataset
ogun = 'https://datasets.mojaglobal.workers.dev/0:/Administrative/Boundaries/Level4%20by%20State/NGA/NGA_AL4_Ogun.json'
crossriver= 'https://datasets.mojaglobal.workers.dev/0:/Administrative/Boundaries/Level4%20by%20State/NGA/NGA_AL4_Cross%20River.json'

# download the data with request
ogun_req = requests.get(ogun).content
crossriver_req = requests.get(crossriver).content

# load the json with geopandas and set crs
ogunboundary_df = gpd.read_file(io.StringIO(ogun_req.decode('utf-8')))
ogunboundary_df = ogunboundary_df[['name', 'geometry']]
ogunboundary_df.set_crs(epsg=4326, inplace=True)
crossriverboundary_df = gpd.read_file(io.StringIO(crossriver_req.decode('utf-8')))
crossriverboundary_df = crossriverboundary_df[['name', 'geometry']]
crossriverboundary_df.set_crs(epsg=4326, inplace=True)

# fix the georeference! the map seems to be cliped from a bad reference
crossriverboundary_df.to_crs(5389, inplace=True)
crossriverboundary_df.to_crs(4326, inplace=True)

print('Ogun dataset has %2d rows and %2d columns' %(ogunboundary_df.shape[0], ogunboundary_df.shape[1]))
print('crossriver dataset has %2d rows and %2d columns' %(crossriverboundary_df.shape[0], crossriverboundary_df.shape[1]))

Ogun dataset has  1 rows and  2 columns
crossriver dataset has  1 rows and  2 columns
In [15]:
crossriverboundary_df
Out[15]:
name geometry
0 Cross River MULTIPOLYGON (((8.14156 4.95736, 8.14143 4.957...
In [16]:
# Using pandas to concatenate the dataframes.
regionsBoundary_df =  pd.concat([crossriverboundary_df, ogunboundary_df], ignore_index=True)
In [17]:
# features for plot
regionsBoundary_df['centroid'] = regionsBoundary_df.geometry.centroid # adding centroid for plotting names in the map

C:\Users\HP\AppData\Local\Temp\ipykernel_17432\2962954293.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  regionsBoundary_df['centroid'] = regionsBoundary_df.geometry.centroid # adding centroid for plotting names in the map

Plotting the map of ogun and crossriver¶

In [18]:
# Plotting in the maps
fig, axes = plt.subplots(1,3, figsize=(20,50))

# first map axes[0]
axes[0].set_title('Nigeria\n', fontsize=20)
boundary_df.plot(ax=axes[0], color='#2E933C', edgecolor='black')
regionsBoundary_df.plot(ax=axes[0], column='name', cmap='Set1', edgecolor='black')

# names in the map
props = dict(boxstyle='round', facecolor='linen', alpha=1)
for point in regionsBoundary_df.iterrows():
  axes[0].text(point[1]['centroid'].x,
          point[1]['centroid'].y,
          point[1]['name'],
          horizontalalignment='center',
          fontsize=10,
          bbox=props)

# second map axes[1]
axes[1].set_title('Ogun\n', fontsize=20)
ogunboundary_df.plot(ax=axes[1], color='grey', edgecolor='black')

# third map axes[2]
axes[2].set_title('crossriver\n', fontsize=20)
crossriverboundary_df.plot(ax=axes[2], color='#EF3E36', edgecolor='black')

plt.show()

Soil resources¶

2.1 World Soil Resources¶

In [19]:
# World Soil resources
soil1 = 'https://datasets.mojaglobal.workers.dev/0:/Soil/World%20Soil%20Resources/World_Soil_Resources_wgs84.geojson'

# download the data with request
soil1_req = requests.get(soil1).content

# load the json with geopandas
soil_df = gpd.read_file(io.StringIO(soil1_req.decode('utf-8')))
soil_df.set_crs(epsg=4326, inplace=True)

# fixing the data: standardizing column names
soil_df.columns = soil_df.columns.str.lower()

print('World Soil resources dataset has %2d rows and %2d columns' %(soil_df.shape[0], soil_df.shape[1]))

World Soil resources dataset has 33 rows and  3 columns

Graphical representation of World Soil Resources¶

In [20]:
# plotting the map
ax = soil_df.plot(figsize = (20,10), column = 'ipcc', cmap='tab20', legend= True, 
                  legend_kwds={'loc': 'lower left', 'ncol': 3})
ax.set_title('WORLD SOIL RESOURCES\n', fontsize=20)

plt.axis('off')
plt.show()

Africa Soil Resources¶

In [21]:
# Using geopandas to clip soil_df in the shape of south_america_df
africasoil_df = soil_df.clip(africa_df)
africasoil_df= africasoil_df.reset_index(drop=True) # removing index
In [22]:
africasoil_df.head(2)
Out[22]:
sname ipcc geometry
0 GL Gleysol MULTIPOLYGON (((38.28120 18.03124, 38.19812 17...
1 WAT Waterbodies MULTIPOLYGON (((-3.96073 5.27866, -4.01422 5.3...

Graphical representation of Africa Soil Resources¶

In [23]:
# plotting the map
ax = africasoil_df.plot(figsize = (30,10), column = 'ipcc', cmap='tab20', legend= True, 
                  legend_kwds={'loc': 'lower left','ncol': 2})
ax.set_title('\nSoil Resources In Africa\n', fontsize=20)

plt.axis('off')
plt.show()

Nigerian Soil Resources¶

In [24]:
# Peru Soil resources
Nigeriansoil1 = 'https://datasets.mojaglobal.workers.dev/0:/Soil/World%20Soil%20Resources/byCountry/NGA_AL2_Nigeria_WSR.json'

# download the data with request
Nigeriansoil1_req = requests.get(Nigeriansoil1).content

# load the json with geopandas
Nigeriansoil1_df = gpd.read_file(io.StringIO(Nigeriansoil1_req.decode('utf-8')))

# fix the georeference the map seems to be cliped from a bad reference
Nigeriansoil1_df.to_crs(5389, inplace=True)
Nigeriansoil1_df.to_crs(4326, inplace=True) # now is WGS84

# fixing the data
# standardizing column names
Nigeriansoil1_df.columns = Nigeriansoil1_df.columns.str.lower()

# drop useless columns
Nigeriansoil1_df = Nigeriansoil1_df[['sname', 'ipcc', 'geometry']]

print('Nigerian Soil resources dataset has %2d rows and %2d columns' %(Nigeriansoil1_df.shape[0], Nigeriansoil1_df.shape[1]))

Nigerian Soil resources dataset has  7 rows and  3 columns
In [25]:
Nigeriansoil1_df.head(2)
Out[25]:
sname ipcc geometry
0 AC Acrisol MULTIPOLYGON (((11.51914 6.61451, 11.51960 6.6...
1 AR Arenosol POLYGON ((5.83178 13.76125, 5.78032 13.78030, ...
In [26]:
# plotting the map
ax = Nigeriansoil1_df.plot(figsize = (10,10), column = 'ipcc',
                  edgecolor='black', cmap='Dark2', legend= True, 
                  legend_kwds= {'loc': 'lower right'})
ax.set_title('\nSoil Resources In Nigeria\n', fontsize=20)

plt.axis('off')
plt.show()

Soil resources for Crossriver¶

In [27]:
# state (Departamento) dataset
Crossriver2 = 'https://datasets.mojaglobal.workers.dev/0:/Soil/World%20Soil%20Resources/byStateWSR/NGA/NGA_AL4_Cross%20River_WSR.json'
Ogun2 = 'https://datasets.mojaglobal.workers.dev/0:/Soil/World%20Soil%20Resources/byStateWSR/NGA/NGA_AL4_Ogun_WSR.json'

# download the data with request
Crossriver2_req = requests.get(Crossriver2).content
Ogun2_req = requests.get(Ogun2).content

# load the json with geopandas and set_crs
CrossriverSoil_df = gpd.read_file(io.StringIO(Crossriver2_req.decode('utf-8')))
OgunSoil_df = gpd.read_file(io.StringIO(Ogun2_req.decode('utf-8')))

# fix the georeference! the map seems to be cliped from a bad reference
CrossriverSoil_df.to_crs(5389, inplace=True)
CrossriverSoil_df.to_crs(4326, inplace=True)
OgunSoil_df.to_crs(5389, inplace=True)
OgunSoil_df.to_crs(4326, inplace=True)

# fixing the data
# standardizing column names
CrossriverSoil_df.columns = CrossriverSoil_df.columns.str.lower()
OgunSoil_df.columns = OgunSoil_df.columns.str.lower()

# drop useless columns
CrossriverSoil_df = CrossriverSoil_df[['sname', 'ipcc', 'geometry']]
OgunSoil_df = OgunSoil_df[['sname', 'ipcc', 'geometry']]

print('Crossriver\'s Soil dataset has %2d rows and %2d columns' %(CrossriverSoil_df.shape[0], CrossriverSoil_df.shape[1]))
print('Ogun\'s Soil dataset has %2d rows and %2d columns' %(OgunSoil_df.shape[0], OgunSoil_df.shape[1]))

Crossriver's Soil dataset has  3 rows and  3 columns
Ogun's Soil dataset has  2 rows and  3 columns
In [28]:
CrossriverSoil_df
Out[28]:
sname ipcc geometry
0 AC Acrisol POLYGON ((8.54653 6.75898, 8.54413 6.75859, 8....
1 FL Fluvisol MULTIPOLYGON (((8.65211 4.91600, 8.56842 4.997...
2 LX Lixisol POLYGON ((9.38266 6.61721, 9.38165 6.61849, 9....
In [29]:
OgunSoil_df
Out[29]:
sname ipcc geometry
0 AC Acrisol POLYGON ((3.08021 7.44243, 3.04750 7.45014, 2....
1 LX Lixisol MULTIPOLYGON (((3.08021 7.44243, 3.07938 7.445...

Soil resources Regions¶

In [30]:
# Using pandas to concatenate the dataframes.
regionSoil_df =  pd.concat([OgunSoil_df, CrossriverSoil_df], ignore_index=True)
In [31]:
# Plotting in the maps
fig, axes = plt.subplots(1,3, figsize=(20,50))

# first map axes[0]
axes[0].set_title('\nSoil Resources In - REGION\n', fontsize=15)
boundary_df.plot(ax=axes[0], color='#2E933C', edgecolor='black')
regionSoil_df.plot(ax=axes[0], column='ipcc', cmap='Dark2', edgecolor='black',
                    legend=True, legend_kwds={'loc': 'lower right'})

# Second map axes[2]
axes[1].set_title('\nSoil Resources In Crossriver\n', fontsize=15)
CrossriverSoil_df.plot(ax=axes[1], column='ipcc', cmap='Dark2', edgecolor='black',
                  legend=True, legend_kwds={'loc': 'lower right'})

# third map axes[2]
axes[2].set_title('\nSoil Resources In Ogun\n', fontsize=15)
OgunSoil_df.plot(ax=axes[2], column='ipcc', cmap='Dark2', edgecolor='black',
                  legend=True, legend_kwds={'loc': 'upper right'})

plt.show()

Bioclimatic & Ecological Zone¶

Biodiversity Hotspots¶

World Biodiversity Hotspots¶

In [32]:
# Biodiversity Hotspots dataset
bioHot = 'https://datasets.mojaglobal.workers.dev/0:/Bioclimatic&EcologicalZones/CI_BiodiversityHotspots.geojson'

# download the data with request
bioHot_req = requests.get(bioHot).content

# load the json with geopandas
bioHot_df = gpd.read_file(io.StringIO(bioHot_req.decode('utf-8')))
bioHot_df.set_crs(epsg=4326, inplace=True)

# fixing the data: standardizing column names
bioHot_df.columns = bioHot_df.columns.str.lower()

print('The Biodiversity Hotspots dataset has %2d rows and %2d columns' %(bioHot_df.shape[0], bioHot_df.shape[1]))

The Biodiversity Hotspots dataset has 53 rows and  6 columns
In [33]:
bioHot_df.head()
Out[33]:
objectid name type shape_length shape_area geometry
0 1 Chilean Winter Rainfall and Valdivian Forests hotspot area 200.706891 40.754665 MULTIPOLYGON (((-74.59792 -46.92502, -74.61111...
1 2 Chilean Winter Rainfall and Valdivian Forests outer limit 191.174978 186.085694 POLYGON ((-70.30946 -22.96028, -70.48596 -22.7...
2 3 Cape Floristic Region hotspot area 93.790123 7.610383 MULTIPOLYGON (((23.08672 -34.07310, 23.08538 -...
3 4 Maputaland-Pondoland-Albany hotspot area 49.631419 25.366584 MULTIPOLYGON (((24.48767 -34.10045, 24.48800 -...
4 5 New Caledonia hotspot area 24.637100 1.646954 MULTIPOLYGON (((167.46053 -22.71228, 167.46161...
In [34]:
# Plotting the map
ax = world_df.plot(figsize=(30,30), color='none', edgecolor='grey', alpha=0.3)
bioHot_df.plot(ax = ax, column= 'type', cmap= 'winter', edgecolor= 'black', legend= True, 
                   legend_kwds={'loc': 'upper center', 'ncol': 15})
ax.set_title('\nWorld Biodiversity Hotspot (By Hotspot Type)\n', fontsize=30)

plt.axis('off')
plt.show()
In [35]:
# Plotting the map
ax = world_df.plot(figsize=(30,30), color='none', edgecolor='grey', alpha=0.3)
bioHot_df.plot(ax = ax, column= 'name', cmap= 'tab10', edgecolor= 'black', legend= True, 
                       legend_kwds={'loc': 'lower center', 'ncol': 6})
ax.set_title('\nWorld Biodiversity Hotspot (By Hotspot Name)\n', fontsize=30)

plt.axis('off')
plt.show()

Africa Biodiversity Hotspots¶

In [36]:
# creating dataframe
AfricaBioHot_df = bioHot_df.clip(africa_df)
AfricaBioHot_df = AfricaBioHot_df.reset_index(drop=True)
In [37]:
# Plotting in the maps
fig, axes = plt.subplots(1,2, figsize=(20,50))

# first map axes[0]
axes[0].set_title('\nAfrican Biodiversity Hotspots (By Hotspot Name)\n', fontsize=15)
africa_df.plot(ax=axes[0], color='none', edgecolor='grey', alpha=0.3)
AfricaBioHot_df.plot(ax=axes[0], column = 'name', cmap='tab20', legend= True, 
                  legend_kwds={'loc': 'lower left', 'ncol': 1})

# second map axes[1]
axes[1].set_title('\nAfrican Biodiversity Hotspots (By Hotspot Type)\n', fontsize=15)
africa_df.plot(ax=axes[1], color='none', edgecolor='grey', alpha=0.3)
AfricaBioHot_df.plot(ax=axes[1], column = 'type', cmap='tab20', legend= True, 
                  legend_kwds={'loc': 'lower right'})
plt.axis('off')
plt.show()

Nigerian Biodiversity Hotspots¶

In [38]:
# creating dataframe
NigeriaBioHot_df = bioHot_df.clip(boundary_df)
NigeriaBioHot_df = NigeriaBioHot_df.reset_index(drop=True)
In [39]:
# Plotting in the maps
fig, axes = plt.subplots(1,2, figsize=(20,50))

# first map axes[0]
axes[0].set_title('\nNigeria Biodiversity Hotspot View (By Hotspot Name)\n', fontsize=15)
boundary_df.plot(ax=axes[0], color='none', edgecolor='grey', alpha=0.3)
NigeriaBioHot_df.plot(ax=axes[0], column = 'name', cmap='Set1', edgecolor='black', legend= True, 
                  legend_kwds={'loc': 'lower right'})

# second map axes[1]
axes[1].set_title('\nNigeria Biodiversity Hotspot View (By Hotspot Type)\n', fontsize=15)
boundary_df.plot(ax=axes[1], color='none', edgecolor='grey', alpha=0.3)
NigeriaBioHot_df.plot(ax=axes[1], column = 'type', cmap='Set1', edgecolor='black', legend= True, 
                  legend_kwds={'loc': 'lower right'})

plt.show()

Biodiversity Hotspots of ogun and crossriver¶

In [40]:
# creating dataframe
CrossriverBioHot_df = bioHot_df.clip(crossriverboundary_df)
CrossriverBioHot_df = CrossriverBioHot_df.reset_index(drop=True)
ogunBioHot_df = bioHot_df.clip(ogunboundary_df)
ogunBioHot_df = ogunBioHot_df.reset_index(drop=True)
In [41]:
# Using pandas to concatenate the dataframes.
regionsBioHot_df = pd.concat([ogunBioHot_df, CrossriverBioHot_df], ignore_index=True)

Plotting Biodiversity Hotspot By Name¶

In [42]:
# Plotting in the maps
fig, axes = plt.subplots(1,3, figsize=(20,50))

# first map axes[0]
axes[0].set_title('\nBiodiversity Hotspot In REGION\n', fontsize=15)
boundary_df.plot(ax=axes[0], color='none', edgecolor='black')
regionsBoundary_df.plot(ax=axes[0], color='none', edgecolor='grey',  alpha=0.4)
regionsBioHot_df.plot(ax=axes[0], column='name', cmap='Set1', edgecolor='black',
                    legend=True, legend_kwds={'loc': 'lower right'})

# second map axes[1]
axes[1].set_title('\nBiodiversity Hotspot In Crossriver (By Hotspot Name)\n', fontsize=15)
crossriverboundary_df.plot(ax=axes[1], color='none', edgecolor='grey',  alpha=0.4)
CrossriverBioHot_df.plot(ax=axes[1], column='name', cmap='Set1', edgecolor='black',
                  legend=True, legend_kwds={'loc': 'lower right'})

# third map axes[2]
axes[2].set_title('\nBiodiversity Hotspot In Ogun (By Hotspot Name)\n', fontsize=15)
ogunboundary_df.plot(ax=axes[2], color='none', edgecolor='grey',  alpha=0.4)
ogunBioHot_df.plot(ax=axes[2], column='name', cmap='Set1', edgecolor='black',
                  legend=True, legend_kwds={'loc': 'upper right'})

plt.show()

Plotting Biodiversity Hotspot By Type¶

In [43]:
# Plotting in the maps
fig, axes = plt.subplots(1,3, figsize=(20,20))

# first map axes[0]
axes[0].set_title('\nBiodiversity Hotspot In REGION\n', fontsize=15)
boundary_df.plot(ax=axes[0], color='none', edgecolor='black')
regionsBoundary_df.plot(ax=axes[0], color='none', edgecolor='grey',  alpha=0.4)
regionsBioHot_df.plot(ax=axes[0], column='type', cmap='summer', edgecolor='black',
                    legend=True, legend_kwds={'loc': 'lower right'})

# second map axes[1]
axes[1].set_title('\nBiodiversity Hotspot In Crossriver (By Hotspot Type)\n', fontsize=15)
crossriverboundary_df.plot(ax=axes[1], color='none', edgecolor='grey',  alpha=0.4)
CrossriverBioHot_df.plot(ax=axes[1], column='type', cmap='summer', edgecolor='black',
                  legend=True, legend_kwds={'loc': 'lower right'})

# third map axes[2]
axes[2].set_title('\nBiodiversity Hotspot In Ogun (By Hotspot Type)\n', fontsize=15)
ogunboundary_df.plot(ax=axes[2], color='none', edgecolor='grey',  alpha=0.4)
ogunBioHot_df.plot(ax=axes[2], column='type', cmap='summer', edgecolor='black',
                  legend=True, legend_kwds={'loc': 'upper right'})

plt.show()

Agro ecological zones¶

World Agro ecological zones¶

In [44]:
# All Agro Ecological Zones dataset
agroEco = 'https://datasets.mojaglobal.workers.dev/0:/Bioclimatic&EcologicalZones/GAEZ_Global_AgroEcological_Zones/GlobalAgroEcologicalZones_GAEZ.geojson'

# download the data with request
agroEco_req = requests.get(agroEco).content

# load the json with geopandas
agroEco_df = gpd.read_file(io.StringIO(agroEco_req.decode('utf-8')))
agroEco_df.set_crs(epsg=4326, inplace=True)

print('Global Agro Ecological Zones dataset has %2d rows and %2d columns.' %(agroEco_df.shape[0], agroEco_df.shape[1]))

Global Agro Ecological Zones dataset has 116675 rows and  2 columns.
In [45]:
agroEco_df.head()
Out[45]:
Value geometry
0 1 POLYGON ((-33.50000 83.65000, -33.50000 83.500...
1 1 POLYGON ((-37.85000 83.60000, -37.85000 83.500...
2 1 POLYGON ((-32.35000 83.60000, -32.35000 83.500...
3 1 POLYGON ((-31.25000 83.60000, -31.25000 83.500...
4 1 POLYGON ((-34.25000 83.60000, -34.25000 83.500...
In [46]:
# fixing the data
values = {'1': 'Wheat', '2': 'Rice', '3': 'Maize', '4': 'Sorghum', '5': 'Millet', 
          '6': 'Other cereals', '7': 'Tubers', '8': 'Roots', '9': 'Sugar beet', 
          '10': 'Sugarcane', '11': 'Pulses', '12': 'Soybean', '13': 'Rape', 
          '14': 'Sunflower', '15': 'Groundnut', '16': 'Oil Palm', '17': 'Olive', 
          '18': 'Cotton', '19': 'Cash 1', '20': 'Vegetables', '21': 'Cash 2', 
          '22': 'Fodder', '23': 'Residual'}

# standardizing column names
agroEco_df.columns = agroEco_df.columns.str.lower()

# renaming class_name
agroEco_df.value = agroEco_df.value.astype('str').replace(values)

Plotting the map¶

In [47]:
# plotting the map
ax = agroEco_df.plot(figsize = (30,25), column = 'value',
                     legend= True, legend_kwds= {'loc': 'lower left','ncol': 2}, cmap='Set1')
ax.set_title('\nWorld Agro-Ecological Zones Map\n', fontsize=25)

plt.axis('off')
plt.show()

Agro ecological zones in Africa¶

In [48]:
# creating dataframe
africaagroEco_df = agroEco_df.clip(africa_df)
africaagroEco_df = africaagroEco_df.reset_index(drop=True)

Plotting Maps¶

In [71]:
# Plotting in the maps
ax = africaagroEco_df.plot(figsize=(10,10), edgecolor='black', column = 'value', cmap='Paired',
                                    legend= True, legend_kwds={'loc': 'lower left'})
ax.set_title('\nAgro ecological zones in Africa\n', fontsize=20)

plt.axis('off')
plt.show()

  • Quite interesting to note the vast amount of Maize domination in some certain areas in africa, Egypt inclusive.

  • It is also worthy of nothing that africa is diversed with differen agro ecology.

    • Agro ecological zones of Nigeria¶

    In [50]:
    # creating dataframe
NigeriaagroEco_df = agroEco_df.clip(boundary_df)
NigeriaagroEco_df = NigeriaagroEco_df.reset_index(drop=True)
    In [51]:
    # Plotting in the maps
ax = NigeriaagroEco_df.plot(figsize=(12.5,12.5), edgecolor='black', column = 'value', cmap='tab20',
                                    legend= True, legend_kwds={'loc': 'lower right','ncol': 3})
ax.set_title('\nAgro ecological zones of Nigeria\n', fontsize=25)

plt.axis('off')
plt.show()

    Agro ecological zones of Crossriver and Ogun¶

    In [52]:
    # creating dataframe
crossriveragroEco_df = agroEco_df.clip(crossriverboundary_df)
crossriveragroEco_df = crossriveragroEco_df.reset_index(drop=True)
ogunagroEco_df = agroEco_df.clip(ogunboundary_df)
ogunagroEco_df = ogunagroEco_df.reset_index(drop=True)

    Agro ecological zones of of both States¶

    In [53]:
    # Using pandas to concatenate the dataframes.
regionsagroEco_df = pd.concat([crossriveragroEco_df, ogunagroEco_df], ignore_index=True)
    In [54]:
    # Plotting in the maps
fig, axes = plt.subplots(1,3, figsize=(20,20))

# first map axes[0]
axes[0].set_title('\nAgro-ecological Zones - REGION\n', fontsize=20)
boundary_df.plot(ax=axes[0], color='#2E933C', edgecolor='black')
regionsagroEco_df.plot(ax=axes[0], column='value', cmap='cividis', edgecolor='black',
                    legend=True, legend_kwds={'loc': 'lower right','ncol': 1})

# second map axes[1]
axes[1].set_title('\nAgro-ecological Zones In Crossriver\n', fontsize=20)
crossriveragroEco_df.plot(ax=axes[1], column='value', cmap='cividis', edgecolor='black',
                  legend=True, legend_kwds={'loc': 'lower right'})

# third map axes[2]
axes[2].set_title('\nAgro-ecological Zones In Ogun\n', fontsize=20)
ogunagroEco_df.plot(ax=axes[2], column='value', cmap='cividis', edgecolor='black',
                  legend=True, legend_kwds={'loc': 'upper right'})

plt.axis('off')
plt.show()

    We have more oil Palm & Groundnut predominant in Crossriver, while olive and Rape are predominant in Ogun State

    Land Cover¶

    Planted Forest in Nigeria¶

    In [55]:
    # Nigerian Planted Forest dataset
forest = 'https://datasets.mojaglobal.workers.dev/0:/LandCover/Forest/Planted_Forests_SDPT/NGA_Nigeria_SDPT.json'

# download the data with request
forest_req = requests.get(forest).content

# load the json with geopandas
forest_df = gpd.read_file(io.StringIO(forest_req.decode('utf-8')))
forest_df.to_crs(epsg=4326)

# fixing the data
# removing Unkown data
forest_df = forest_df.replace('Unknown', np.nan)
forest_df = forest_df.replace('n/a', np.nan)

# subseting important variables
forest_df = forest_df.rename(columns = {'size':'size_tree'})
forest_df = forest_df[['species', 'species_simp', 'common_name', 'plant_ag', 
                       'timber_ag','ever_dec', 'conifer_broad', 'hard_soft', 
                       'size_tree', 'year', 'org_name', 'geometry']]

print('Nigerian Planted Forest dataset has %2d rows and %2d columns' %(forest_df.shape[0], forest_df.shape[1]))

    Nigerian Planted Forest dataset has 85 rows and 12 columns
    In [56]:
    forest_df.head()
    Out[56]:
    species species_simp common_name plant_ag timber_ag ever_dec conifer_broad hard_soft size_tree year org_name geometry
    0 Elaeis guineensis Oil Palm Oil Palm Tree crops Tree crops NaN NaN NaN NaN 2013-2015 oil palm POLYGON ((6.73408 5.07584, 6.73217 5.09476, 6....
    1 Elaeis guineensis Oil Palm Oil Palm Tree crops Tree crops NaN NaN NaN NaN 2013-2015 oil palm POLYGON ((6.93543 5.18647, 6.90801 5.18638, 6....
    2 Elaeis guineensis Oil Palm Oil Palm Tree crops Tree crops NaN NaN NaN NaN 2013-2015 oil palm POLYGON ((6.82456 5.30529, 6.82322 5.30431, 6....
    3 Elaeis guineensis Oil Palm Oil Palm Tree crops Tree crops NaN NaN NaN NaN 2013-2015 oil palm POLYGON ((6.82203 5.31630, 6.82144 5.31719, 6....
    4 Elaeis guineensis Oil Palm Oil Palm Tree crops Tree crops NaN NaN NaN NaN 2013-2015 oil palm POLYGON ((6.78512 5.32028, 6.78444 5.32236, 6....
    In [57]:
    # Plotting in the maps
fig, axes = plt.subplots(1,4, figsize=(20,20))

# first map axes[0]
axes[0].set_title('\nPlanted Forest In Nigeria (By Common Name)\n')
boundary_df.plot(ax=axes[0], color='#2E933C', edgecolor='black')
regionsBoundary_df.plot(ax=axes[0], color='none', edgecolor='grey', alpha=0.3)
forest_df.plot(ax=axes[0], column='common_name', edgecolor='black', markersize=15, cmap='RdPu',
             legend=True, legend_kwds={'loc': 'lower right'})

# second map axes[1]
axes[1].set_title('\nPlanted Forest In Nigeria (By Common Timber)\n')
boundary_df.plot(ax=axes[1], color='#2E933C', edgecolor='black')
regionsBoundary_df.plot(ax=axes[1], color='none', edgecolor='grey', alpha=0.3)
forest_df.plot(ax=axes[1], column='timber_ag', edgecolor='black', markersize=15, cmap='Paired',
             legend=True, legend_kwds={'loc': 'lower right'})

# third map axes[2]
axes[2].set_title('\nPlanted Forest In Nigeria (By timber_ag)\n')
boundary_df.plot(ax=axes[2], color='#2E933C', edgecolor='black')
regionsBoundary_df.plot(ax=axes[2], color='none', edgecolor='grey', alpha=0.3)
forest_df.plot(ax=axes[2], column='timber_ag', edgecolor='black', markersize=15, cmap='Set3',
             legend=True, legend_kwds={'loc': 'lower right'})

# third map axes[2]
axes[3].set_title('\nPlanted Forest In Nigeria (By Type of Tree)\n')
boundary_df.plot(ax=axes[3], color='#2E933C', edgecolor='black')
regionsBoundary_df.plot(ax=axes[3], color='none', edgecolor='grey', alpha=0.3)
forest_df.plot(ax=axes[3], column='species_simp', edgecolor='black', markersize=15, cmap='Set1_r',
             legend=True, legend_kwds={'loc': 'lower right'})

plt.axis('off')
plt.show()

    Climate¶

    World Climate Zones¶

    In [58]:
    # Climate Zones dataset
climate = 'https://datasets.mojaglobal.workers.dev/0:/Climate/IPCC_ClimateZoneMap_Vector.geojson'

# download the data with request
climate_req = requests.get(climate).content

# load the json with geopandas
climate_df = gpd.read_file(io.StringIO(climate_req.decode('utf-8')))
climate_df.set_crs(epsg=4326, inplace=True)

# standardizing column names
climate_df.columns = climate_df.columns.str.lower()

print('World Climate Zones dataset has %2d rows and %2d columns.' %(climate_df.shape[0], climate_df.shape[1]))

    World Climate Zones dataset has 26165 rows and  2 columns.
    In [59]:
    climate_df.head()
    Out[59]:
    class_name geometry
    0 0 MULTIPOLYGON (((-37.65000 83.50000, -37.65000 ...
    1 0 MULTIPOLYGON (((-38.75000 83.40000, -38.75000 ...
    2 0 MULTIPOLYGON (((-38.60000 83.40000, -38.60000 ...
    3 0 MULTIPOLYGON (((-37.15000 83.40000, -37.15000 ...
    4 5 MULTIPOLYGON (((-40.40000 83.40000, -40.40000 ...
    In [60]:
    # Replacing the data that was in codes
classification =  {'1': 'Tropical montane', '2' : 'Tropical wet', 
                   '3': 'Tropical moist', '4': 'Tropical dry', 
                   '5': 'Warm temperate moist', '6': 'Warm temperate dry',
                   '7': 'Cool temperate moist', '8': 'Cool temperate dry',
                   '9': 'Boreal moist', '10': 'Boreal dry', '11' : 'Polar moist',
                   '12': 'Polar dry'}


# renaming class_name
climate_df.class_name = climate_df.class_name.astype('str').replace(classification)

# removing oceans
climate_df =  climate_df.loc[climate_df.class_name != '0']
    In [61]:
    climate_df.head()
    Out[61]:
    class_name geometry
    4 Warm temperate moist MULTIPOLYGON (((-40.40000 83.40000, -40.40000 ...
    5 Warm temperate moist MULTIPOLYGON (((-41.60000 83.35000, -41.60000 ...
    6 Warm temperate moist MULTIPOLYGON (((-40.75000 83.25000, -40.75000 ...
    10 Warm temperate dry MULTIPOLYGON (((-76.25000 83.10000, -76.25000 ...
    11 Warm temperate dry MULTIPOLYGON (((-75.10000 83.10000, -75.10000 ...

    plotting the map¶

    In [62]:
    # plotting the map
ax = climate_df.plot(figsize = (25,30), column = 'class_name', legend= True, cmap='Spectral',
                     legend_kwds= {'loc': 'lower left', 'ncol': 3})
ax.set_title('WORLD CLIMATE ZONE MAP\n', fontsize=25)

plt.axis('off')
plt.show()

    Climate Zones of Africa¶

    In [63]:
    # creating dataframe
africaClimate_df = climate_df.clip(africa_df)
africaClimate_df = africaClimate_df.reset_index(drop=True)
    In [64]:
    # Plotting in the maps
ax = africaClimate_df.plot(figsize=(10,10), edgecolor='black', column = 'class_name', cmap='tab20',
                                    legend= True, legend_kwds={'loc': 'lower left'})
ax.set_title('\nCLIMATE ZONES OF AFRICA\n', fontsize=20)

plt.axis('off')
plt.show()

    Climate Zones of Nigeria¶

    In [65]:
    # creating dataframe
NigeriaClimate_df = climate_df.clip(boundary_df)
NigeriaClimate_df = NigeriaClimate_df.reset_index(drop=True)
    In [66]:
    # Plotting in the maps
ax = NigeriaClimate_df.plot(figsize=(10,15), edgecolor='black', column = 'class_name', cmap='tab20',
                                    legend= True, legend_kwds={'loc': 'lower left'})
ax.set_title('\nCLIMATE ZONES OF Nigeria\n', fontsize=20)
plt.show()

    Climate zones of Crossriver and Ogun¶

    In [67]:
    # creating dataframe
crossriverClimate_df = climate_df.clip(crossriverboundary_df)
crossriverClimate_df = crossriverClimate_df.reset_index(drop=True)
ogunClimate_df = climate_df.clip(ogunboundary_df)
ogunClimate_df = ogunClimate_df.reset_index(drop=True)
    In [68]:
    # Using pandas to concatenate the dataframes.
regionsClimate_df = pd.concat([crossriverClimate_df, ogunClimate_df], ignore_index=True)
    In [69]:
    # Plotting in the maps
fig, axes = plt.subplots(1,3, figsize=(20,20))

# first map axes[0]
axes[0].set_title('CLIMATIC ZONES IN THE REGION\n', fontsize=20)
boundary_df.plot(ax=axes[0], color='#2E933C', edgecolor='black')
regionsClimate_df.plot(ax=axes[0], column='class_name', cmap='tab20', edgecolor='black',
                    legend=True, legend_kwds={'loc': 'lower left'})

# second map axes[1]
axes[1].set_title('\nCLIMATIC ZONES IN Crossriver\n', fontsize=20)
crossriverClimate_df.plot(ax=axes[1], column='class_name', cmap='tab20', edgecolor='black',
                  legend=True, legend_kwds={'loc': 'lower left'})

# third map axes[2]
axes[2].set_title('\nCLIMATIC ZONES IN Ogun\n', fontsize=20)
ogunClimate_df.plot(ax=axes[2], column='class_name', cmap='tab20', edgecolor='black',
                  legend=True, legend_kwds={'loc': 'upper right'})
plt.show()
    • Climatic zones in Crossriver and Ogun are: Boreal dry, Boreal Moist, Polar Moist with Polar Moist specifically the only dominant climatic zone in Ogun

    Conclusion¶

    • We have more oil Palm & Groundnut predominant in Crossriver, while olive and Rape are predominant in Ogun State
    • Quite interesting to note the vast amount of Maize domination in some certain areas in africa, Egypt inclusive.
    • It is also worthy of nothing that africa is diversed with different agro ecology.
    • Climatic zones in Crossriver and Ogun are: Boreal dry, Boreal Moist, Polar Moist with Polar Moist specifically the only dominant climatic zone in Ogun
    • Some of the most populated coutries are: South Africa, Nigeria, Egypt, Algeria, Ethopia, Sudan, Tanzania, Democratic republic of congo,uganda, kenya. while djibouti one of the least populated